A Federated Information System for the SDGs

United Nations Statistics Division

CONTEXT

Multilevel SDG data reporting

• National and subnational reporting is the most significant level of the SDG review process

• The global SDG monitoring system builds on national data reporting

– Data derived from national sources is the foundation for SDG reviews at all levels

– It is crucial to create opportunities for countries to directly contribute to global reporting

Decision by Statistical Commission

• At its 49th session in March 2018, the Statistical Commission welcomed the efforts to establish a federated system of national and global data hubs for the SDGs to:

– facilitate integration of different data sources,

– promote data interoperability

– foster collaboration among partners from different stakeholder groups, including the geospatial community and other data providers,

– improve data flows and global reporting of the SDGs.

How does the Federated System work?

• It is a country-led “system of systems”• Implemented through

– Open standards and principles for data interoperability– Geospatial information systems (GIS) and data analytics capabilities– Web-based collaboration, communication and user engagement

• Supports NSOs in managing statistical and geospatial data, integrating new and innovative data sources with traditional ones

• Enables local/national decision makers to access, understand and use SDG data

• Empowers countries to directly contribute to global SDG reporting through innovative applications.

Federated data governance model

• Based on the principle of national ownership,

• National Statistical Systems coordinate the implementation of a common set of global policies, standards and procedures around the production, dissemination and use of data to support the implementation of the 2030 Agenda, while addressing country-specific SDG data needs and priorities.

• Implementation of a multi-layered set of standards and procedures from local to national to global levels.

Federated information systems architecture

• High-level conceptual and logical data models provide a consistent view of SDG-related data across countries and organizations.

• Common data models, data definitions and data flows allow for data interoperability and integration across multiple systems into a network of federated data hubs

– Each hub independently publishes and shares authoritative data using a common schema, thus contributing to a global catalog of standardized open SDG data and information

– Users can access the data they need while the traceability and accountability of the originating data sources is ensured.

Interoperability and web-based collaboration

• The interface to each SDG data hub is based on open standards and the use of common vocabularies for describing and organizing data content.

• Web-based technology enables collaboration within and across organizations. – Anyone within and outside a data provider’s organization can directly

access data and applications made publicly available through the organization’s open data hub

– Users with proper credentials can access content shared with specific user groups.

– Data providers document best practices and share them with other organizations providing a collaborative environment for the entire SDG data life-cycle

UN SDG Data Hub

• Linked to a global network to share data, templates and common initiatives

• Enabled by Web GIS and open standards

Data

Policy

Initiatives

Initiatives

S. Africa

UN SDGHub Network

Ireland

Mexico

Senegal Philippines

Qatar

Partner

PartnerPartner

A network of national SDG Data Hubs

• Supporting national partnerships around data and policy initiatives

• Providing an inclusive and enabling environment for all stakeholders

Data

Policy

Initiatives

Initiatives

Partner

NationalDataHub

Partner

Ministry

Province Academia

City

NGO

PartnerPartner

GLOBAL UN SDG DATA HUBImplementation

http://unstats-undesa.opendata.arcgis.com/

Initial setup

• Setup the technological platform

• Define an authoritative source of geometries that represent geographic location of each record in the data set.

• Define an authoritative source of statistical data and metadata on SDG indicators to be published.

Preparation of datasets for publication

• Extract data for individual SDG indicators and join them with their corresponding geometries

– The structure of the resulting datasets is designed to facilitate the representation of the data in maps (e.g., each row represents one geographic feature, and all areas are included, regardless of whether they have data or not)

Publication

• Collect information describing the nested structure and content of the global SDG indicator framework (code, title, and description of each node on the goal > target > indicator > series hierarchy)

• Collect the list of tags assigned to each series:

• Define renderer parameters for each item (i.e., data field to be mapped by default, color, max/min values, breaks, transparency, etc.)

• Upload each item as a data layer

• Share with open data sites, groups and applications

Publication Process

SDG database SD

G A

PI

R script (csv generator)

SDG icons

Geo Areas (with

coordinates)

Python script (publisher)CSV files

ArcGIS online

Feature layers

(hosted)

13

2

4

5

6

MetadataAPI.json

(incl. tags)

GitHub

https://github.com/UNStats-SDGs/sdg-publisher

SDG API

https://unstats.un.org/SDGAPI/swagger/

1

https://unstats.un.org/SDGAPI/v1/sdg/Series/Data?seriesCode=AG_FPA_MILLET

• General info (size, pages)• List of attributes (id, code lists)• List of dimensions (id, code lists)• Data records:

✓ Goal, Target, Indicator✓ Series (code, description)✓ Geo Area (code, description)✓ Period✓ Value ✓ Footnotes (time detail, source, other footnotes)✓ Attribute values✓ Dimension values

Geo Areas2

R script3 Create “long” and “wide” views of a series data cube:

Geographic Areas

×years

×unique

dimension values(“slice”)

+Values

and attributes

CSV file4